Glucose provides a substantial portion of the human body's energy needs. Because of its importance, the body continuously monitors glucose concentrations and maintains an optimal concentration through a complex interplay of hormones. Insulin dependent diabetes (Type I) occurs as a result of the body's inability to synthesize proper amounts of insulin. This results in carbohydrate protein and lipid catabolism. Insulin replacement is absolutely essential in this form of diabetes. Non-insulin dependent diabetes (Type II) occurs as a result of relative insulin deficiency. Both types of diabetes result in the chronic complications of retinopathy, nephropathy, coronary heart disease, stroke and pheripheral vascular disease.
Treatment of Type I diabetes involves diet, exercise, and insulin replacement in order to minimize the complications of the disease. The amount of insulin replacement is usually determined by periodically monitoring blood glucose levels using commercially available monitoring kits, involving the pricking of fingers, etc. However, even if insulin, diet, and exercise are properly maintained, complications can still result, since monitoring of glucose is not continuous. These sampling gaps emphasize the need for improved monitoring and the development of biosensors for monitoring glucose levels, with the potential for implantation. One type of biosensor is a chemical sensor that uses enzyme molecules which are immobilized in a permeable matrix for real-time measurements over a dynamic range of 0.1 to 20 mmol. Although accurate, chemical sensors share the common problem of requiring relatively frequent replenishment of the enzyme (e.g. every 30 days). Providing such a chemical sensor in vivo presents the obvious problem of frequent surgeries to implant and remove the chemical sensors.
Another problem with chemical sensors is that these sensors will lose their effectiveness, if implanted, when cells grow over the enzymatically impregnated membranes. There is therefore a need for a highly reliable and long-term in vivo non-chemical sensor that will detect glucose concentrations.
Recently, non-chemical sensors that are optical in nature have been proposed. These sensors would avoid the problems of chemical sensors, yet pose problems of their own. An optical sensor for detecting glucose is described in Blood Glucose Measurement By Multiple Attenuated Total Reflection and Infrared Absorption Spectroscopy, by Mendelson, et al., IEEE Transactions on Biomedical Engineering, Vol. 37, No. 5, May 1990. An optical analyzer that uses absorption spectroscopy in the infrared region as described in the above article avoids the need for replenishing enzymes and would also avoid the problem of cell growth over enzymatic membranes.
"Absorption spectroscopy" in the infrared region is a technique based upon the phenomena that each molecule of a biological substance has specific resonance absorption peaks which are known as "fingerprints". These unique characteristic peaks are caused by vibrational and rotational oscillations of the molecules. Biological molecules have very complex structures, and therefore possess a large number of absorption peaks in the infrared region. Many of the absorption peaks occur that overlap those of other molecules that exist in whole blood, such as cholesterol.
There are three basic problems associated with the detection of any biological substance in an aqueous solution using infrared absorption spectroscopy. These problems are: the intrinsic high background absorption of water; the large number of overlapping infrared absorption peaks of other molecules; and the degradation of the signal of interest due to noise that is usually caused by the instrument itself and interference due to other molecules.
Although the device proposed by Mendelson, et al. was able to detect a glucose IR signal in blood, despite the problem of overlapping absorbances, this device has to use high (non-physiological) glucose concentrations and a high energy CO.sub.2 laser source.
There is therefore a need for a method and apparatus that will sense a particular condition (such as the glucose concentration in blood) and distinguish a signal of interest representative of that condition from background noise in the system and in the measurement device. If used as a glucose detector, for example, the method and device needs to be able to detect physiological glucose levels in blood with a low-energy selective source and a robust artificial neural network detection method.